class_docstr: Logger for tensorboard.
  
  Adds interface for `add_image` in the logger rather than calling the experiment object.
  
  Note:
      Same as the Tensorboard Logger provided by PyTorch Lightning and the doc string is reproduced below.
  
  Logs are saved to
  ``os.path.join(save_dir, name, version)``. This is the default logger in Lightning, it comes
  preinstalled.
  
  Example:
      >>> from pytorch_lightning import Trainer
      >>> from anomalib.utils.loggers import AnomalibTensorBoardLogger
      >>> logger = AnomalibTensorBoardLogger("tb_logs", name="my_model")
      >>> trainer = Trainer(logger=logger)
  
  Args:
      save_dir (str): Save directory
      name (Optional, str): Experiment name. Defaults to ``'default'``. If it is the empty string then no
          per-experiment subdirectory is used.
      version (Optional, int, str): Experiment version. If version is not specified the logger inspects the save
          directory for existing versions, then automatically assigns the next available version.
          If it is a string then it is used as the run-specific subdirectory name,
          otherwise ``'version_${version}'`` is used.
      log_graph (bool): Adds the computational graph to tensorboard. This requires that
          the user has defined the `self.example_input_array` attribute in their
          model.
      default_hp_metric (bool): Enables a placeholder metric with key `hp_metric` when `log_hyperparams` is
          called without a metric (otherwise calls to log_hyperparams without a metric are ignored).
      prefix (str): A string to put at the beginning of metric keys.
      **kwargs: Additional arguments like `comment`, `filename_suffix`, etc. used by
          :class:`SummaryWriter` can be passed as keyword arguments in this logger.
code: def __init__(
      self,
      save_dir: str,
      name: str | None = "default",
      version: int | str | None = None,
      log_graph: bool = False,
      default_hp_metric: bool = True,
      prefix: str = "",
      **kwargs,
  ):
      super().__init__(
          save_dir,
          name=name,
          version=version,
          log_graph=log_graph,
          default_hp_metric=default_hp_metric,
          prefix=prefix,
          **kwargs,
      )
  
file_path: anomalib/src/anomalib/utils/loggers/tensorboard.py
imports: from __future__ import annotations
  from typing import Any
  import numpy
  from matplotlib.figure import Figure
  from pytorch_lightning.utilities import rank_zero_only
  from .base import ImageLoggerBase
label_desc: Use image node_id 1 to create a new AnomalibTensorBoardLogger object from inherited base classes: ImageLoggerBase, TensorBoardLogger with example: obj = AnomalibTensorBoardLogger(save_dir, name, version, log_graph, default_hp_metric, prefix)